PV systems present unique challenges to system operators, particularly since these systems are often physically large or distributed over a large geographic area. In utility applications, for example, a PV system may integrate 40,000 to 8 million individual modules along with the corresponding fuses, combiners and conductors. Portfolios of customer-sited PV systems, meanwhile, often include hundreds of distributed systems, many of which are roof mounted, each with its own set of components and site-access restrictions. The sheer scale of these systems and portfolios is inherently challenging for the system operators and asset managers tasked with monitoring and managing these assets and distributed fleets.

The relative inaccuracy of performance measurements and data analysis tools further compounds these issues. According to Sandia National Laboratories’ 2008 report, “Comparison of PV System Performance-Model Predictions with Measured PV System Performance,” the absolute accuracy of data modeling is on the order of 5%–10%, and the accuracy of relative measurements is on the order of 3%–5%. This means that site operators do not have visibility into any on-site performance issues that reduce system output by an amount lower than these margins of uncertainty. To address this lack of visibility, operators often rely on manual field tests—including I-V curve traces and thermal images captured with handheld infrared (IR) cameras—to locate defects within the array. Since these tests are labor intensive and costly, operators generally inspect only 10%–25% of the modules per site annually.

These combined factors mean that PV systems can incur undetected phantom losses, which reduce energy yield and economic performance. System operators can benefit from new methodologies and technologies for detecting system faults. Aerial inspections, which capture IR and visible imagery, approach this problem from an entirely new vantage point—namely, from the air.

Aerial Inspection Process

Manned aircraft, unmanned aerial vehicles or aircraft systems (drones), and even balloons are all potential platforms for the flyover component of an aerial inspection. The main factors influencing vehicle selection include inspection time, accuracy, repeatability and scalability. On one hand, aircraft with a pilot on board can inspect a PV system at a rate of 0.5 MW–1 MW per minute and do not require physical site access or regulatory approval for the flight plan. On the other, unmanned aerial vehicles (UAVs) have slower flying speeds, lower-resolution cameras and limited battery life; as a result, inspections with UAVs take more time and generally require multiple flights per site. A UAV inspection also requires physical site access and regulatory approval for the specific flight path.

Fault detection. Energy balance is the basic principle behind the thermal component of aerial inspections. Generally speaking, the surface of all the modules at a given site receive approximately the same amount of irradiance. Modules that are operating properly convert roughly 15%–20% of this incident energy into electricity. Those modules that are not operating properly convert that same energy into heat. The end result is that underperforming or nonperforming modules are warmer than the surrounding operational modules.

Aerial inspections provide system operators with IR measurements for all the modules in a roof- or ground-mounted PV system. These thermal images allow operators to precisely identify and map underperforming portions of the array. When properly implemented, an aerial inspection campaign can identify a wide range of dc fault mechanisms. As such, aerial inspections can largely replace manual dc measurements as part of an annual preventative maintenance scope of work. IR inspections can detect any fault mode that causes a significant decrease in module output.

Common faults. As shown in Figure 1, the most common fault modes in PV systems deployed with crystalline silicon (c-Si) modules include string-level failures, submodule failures and cell-level hot spots. String-level thermal signatures indicate some type of open-circuit condition, perhaps due to a blown or missing fuse, an open fuseholder or module interconnection, or a failure within a module or source-circuit conductor. Submodule thermal signatures, often involving 33% of the cells within a module, generally indicate that a bypass diode is engaged or has failed. Cell-level hot spots can reveal resistive losses within modules, perhaps due to cell cracking or solder joint deterioration.

Compared to c-Si PV systems, thin-film arrays generally have shorter source circuits and require more series strings for the same power capacity. As a result, aerial inspections tend to reveal relatively higher rates of string failures in thin-film systems. IR inspections of thin-film arrays can also identify isolated hot spots, major differences in module efficiency caused by differential degradation rates, or internal variations in thin-film deposition quality.

To complement the IR imagery, aerial inspections should also capture high-resolution images in the visible spectrum. These conventional aerial images can reveal the presence and distribution of soiling, locations or regions requiring additional vegetation control, physical damage to racking, site erosion, encapsulant degradation or discoloration, and so forth. As shown in Figure 2, investigators can correlate these datasets to add further insight into failure modes and allow more-accurate root cause analysis of failures.

Benefits of Aerial Inspections

The value propositions associated with aerial inspections include more-comprehensive site coverage, enhanced visibility into plant performance issues and improved site safety.

Comprehensive coverage. Operators can use high-quality aerial inspections in lieu of labor-intensive preventative maintenance activities, including manual I-V curve traces, voltage and current measurements, handheld IR thermography, module electrical connection tests and visual inspections. In comparison to these manual tests, aerial inspections not only identify dc performance issues with a higher degree of accuracy (and less labor), but also allow operators to characterize an entire plant under consistent operating conditions.

Where operators rely exclusively on manual preventative maintenance tests, technicians generally characterize only a representative subset of source circuits at a site each year. Because it takes a lot of time to conduct manual tests, especially on multi-megawatt sites that cover hundreds if not thousands of acres, the test conditions are inherently more variable, which complicates the process of comparing and analyzing the results. This piecemeal approach can result in undetected losses.

Enhanced visibility. Heliolytics has inspected more than 2.5 GW of PV projects internationally. We performed a comparative analysis on 1.6 GW of this portfolio across 280 sites, ranging from 60 kW to 250 MW, which is representative of systems from across North America, and filtered that selection to exclude systems with failure rates over 10%. Analyzing these representative data in the aggregate, we find that phantom dc capacity losses are as high as 1.25% of installed capacity across all sites, based on the expected performance impacts of observed faults. The average capacity losses for projects under 10 MW are 1.29% versus 1% for projects over 10 MW. String-level failures account for 84% of the capacity losses, with module-level faults making up the balance of the phantom losses.

Most importantly, all these data come from sites with active O&M and data analysis programs in place. In most cases, technicians had conducted I-V curve traces and handheld IR inspections for 10%–25% of the modules at each site. Therefore, these failure rates represent losses associated with faults that are slipping through the cracks because traditional data analytics and manual inspections are incapable of or ill-suited to identifying all the phantom losses that sap PV system performance and revenue.

By contrast, annual aerial thermal inspection results provide technicians with data that are both granular and highly actionable. Aerial IR imagery tells technicians exactly where to locate and remedy dc performance problems within an array. Because string-level failures are relatively consistent throughout the life of a system and account for the majority of the expected capacity losses, technicians can quickly repair these problems and increase system production.

Since aerial surveys can identify 100% of the faults at a given site, operators can use these data to classify all the fault mechanisms at a site and potentially identify systemic or serial issues. Figure 3, for example, is a map for a 20 MW solar farm where each color corresponds with a specific manufacturing batch and the letter X identifies locations of diode failures. Whereas the overall diode failure rate was only 0.2%, we observed that the majority of these failed diodes were associated with a specific manufacturing batch (dark orange). Identifying this systemic issue allowed the owner to prosecute for warranty remediation proactively, before the data acquisition system even had visibility into the progress of this fault mode.

Site safety. When operators use aerial inspections in lieu of manual dc inspections, technicians spend less time accessing combiner boxes and inverters. This effectively reduces worker exposure to electrical hazards. Technicians are exposed to electrical shock hazards whenever they open a combiner box or inverter; in large-scale systems deployed with central inverters, technicians are also potentially exposed to dc arc-flash hazards. (See “Calculating DC Arc-Flash Hazards in PV Systems,”SolarPro, February/March 2014.)

While it is possible to control these risks with personal protective equipment, there remains opportunity for human error or equipment failure. In the long term, the more effective and sustainable safety practice is to simply eliminate unnecessary manual inspection activities wherever possible. Viewed from this perspective, aerial inspections provide operators and organizations with an opportunity to implement a higher level of hazard control in accordance with OSHA’s hierarchy of controls methodology. Though workers may still need to open combiners, disconnects or inverters to conduct periodic visual and IR inspections, the hazards associated with these visual inspection activities are less severe than the hazards associated with physically accessing busbars and fuseholders to perform electrical characterization tests.

Best Practices

For optimal visibility into plant performance, operators can perform annual aerial inspections that cover 100% of the modules for a given site, then supplement these data with targeted module-level I-V tracing. Technicians should regularly capture supplemental I-V curve traces for a constant subset of modules that represent all of the major serial number batches deployed on-site. For best aerial inspection results, operators should pay careful attention to both data collection and post-survey data processing.

Data collection. Relatively steady high-irradiance conditions are required for the flyover component of an aerial inspection. As is the case with commissioning or performance tests, the minimum irradiance for an aerial inspection is 600 W/m2. Ideally, the irradiance should not vary by more than 100 W/m2 during the survey, as steady-state conditions allow for a better comparison of results across the site.

An aerial survey should collect both IR and visible imagery. The quality of these data must be adequate to allow for fault detection. Data quality depends on both image resolution and sensitivity. Image resolution is a function of the size of each pixel, with smaller pixels resulting in a more detailed image. Sensitivity, meanwhile, is a function of a camera’s ability to distinguish between small variations in temperature (IR camera) or light (conventional camera).

To detect major module faults during an aerial site survey, an IR inspection system needs to have a resolution of at least 19 cm/pixel. It is possible to identify defects on a subcell level, after post-survey data processing, if the IR inspection system has a resolution of at least 15 cm/pixel. To detect minor temperature fluctuations between modules, the IR camera should have a noise equivalent temperature difference (NETD) rating of no more than 20 mK.

For visible measurements, image resolution is the key metric. I recommend using an imaging system with a minimum resolution of 3 cm/pixel. With this level of resolution, it is possible to identify small amounts of surface soiling, such as vegetation or bird droppings, which can cause localized hot spots. The identification of hot spots due to actual cell damage requires data processing to compare IR and thermal imagery, then filter out those hot spots associated with soiling or other external causes.

Data processing. This is the most critical part of an aerial inspection. The data processing methodology must be able to not only detect faults and distinguish between fault modes, but also accurately locate the faults within the array. It is not enough to know that faults exist. For technicians to remediate problems efficiently, aerial survey results must locate faults within the array down to the module level.

Since the processing software traditionally used for non-PV site surveys is not compatible with IR imagery from PV systems, the technician or analyst needs to either process these data manually or process them using custom, proprietary software. Manual data processing requires that someone scroll through the recorded video feeds looking for and locating faults. As a result, this technique is prone to error and may require a follow-up visit to verify results in the field. By comparison, operators can use validated automated techniques directly for field remediation, warranty prosecution and system planning.